





from __future__ import annotations





def 	lowerCAmelCase_					(			__a						)	->						bool:







  """simple docstring"""





  lowerCamelCase__: Union[str, Any]         =len(__a						)
  # We need to create solution object to save path.
  lowerCamelCase__: Optional[Any]         =[[0 for _ in range(__a						)] for _ in range(__a						)]
  lowerCamelCase__: int         =run_maze(__a      ,       0      ,       0      ,       __a						)
  if solved:
    print("\n".join(str(__a						) for row in solutions						)						)
  else:
    print("No solution exists!"						)
  return solved





def 	lowerCAmelCase_					(			__a      ,       __a      ,       __a      ,       __a						)	->						bool:







  """simple docstring"""





  lowerCamelCase__: List[str]         =len(__a						)
  # Final check point.
  if i == j == (size - 1):
    lowerCamelCase__: List[str]         =1
    return True

  lowerCamelCase__: Optional[int]         =(not i < 0) and (not j < 0)  # Check lower bounds
  lowerCamelCase__: Dict         =(i < size) and (j < size)  # Check upper bounds

  if lower_flag and upper_flag:
    # check for already visited and block points.
    lowerCamelCase__: Optional[Any]         =(not solutions[i][j]) and (not maze[i][j])
    if block_flag:
      # check visited
      lowerCamelCase__: Optional[Any]         =1

      # check for directions
      if (
          run_maze(__a      ,       i + 1      ,       __a      ,       __a						)
          or run_maze(__a      ,       __a      ,       j + 1      ,       __a						)
          or run_maze(__a      ,       i - 1      ,       __a      ,       __a						)
          or run_maze(__a      ,       __a      ,       j - 1      ,       __a						)
      ):
        return True

      lowerCamelCase__: List[Any]         =0
      return False
  return False


if __name__ == "__main__":
     import doctest

     doctest.testmod()









"""simple docstring"""


# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput




class 						__UpperCamelCase     (     UpperCamelCase						):
					def __init__(							self							:	str     , UpperCAmelCase							:	Union[str, Any]     , UpperCAmelCase							:	str   )	->       Any:
											super().__init__()
											self.register_modules(unet=UpperCAmelCase     , scheduler=UpperCAmelCase   )
					@torch.no_grad()
					def __call__(							self							:	int     , UpperCAmelCase							:	int = 1     , UpperCAmelCase							:	Optional[torch.Generator] = None     , UpperCAmelCase							:	int = 50     , UpperCAmelCase							:	Optional[str] = "pil"     , UpperCAmelCase							:	bool = True     , **UpperCAmelCase							:	Dict     , )	->       Union[ImagePipelineOutput, Tuple]:
											lowerCAmelCase   :Optional[Any]						=			torch.randn(
											    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)     , generator=UpperCAmelCase     , )
											lowerCAmelCase   :str						=			image.to(self.device   )

											# set step values
											self.scheduler.set_timesteps(UpperCAmelCase   )

											for t in self.progress_bar(self.scheduler.timesteps   ):
																	# 1. predict noise model_output
																	lowerCAmelCase   :Dict						=			self.unet(UpperCAmelCase     , UpperCAmelCase   ).sample

																	# 2. predict previous mean of image x_t-1 and add variance depending on eta
																	# eta corresponds to η in paper and should be between [0, 1]
																	# do x_t -> x_t-1
																	lowerCAmelCase   :List[str]						=			self.scheduler.step(UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase   ).prev_sample

											lowerCAmelCase   :Any						=			(image / 2 + 0.5).clamp(0     , 1   )
											lowerCAmelCase   :Tuple						=			image.cpu().permute(0     , 2     , 3     , 1   ).numpy()
											if output_type == "pil":
																	lowerCAmelCase   :Dict						=			self.numpy_to_pil(UpperCAmelCase   )

											if not return_dict:
																	return (image,), "This is a local test"

											return ImagePipelineOutput(images=UpperCAmelCase   ), "This is a local test"